Can gemma 3 4b it run on NVIDIA L4 24GB?

YES — Runs Great

C46Usable

Estimated from fit model

gemma 3 4b it needs ~6.2 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~64 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 6.2 GB, 64.0 tok/s, Runs well

6.2 GB required24.0 GB available

26% VRAM used

Fit status

Runs well

Decode

64.0 tok/s

TTFT

3025 ms

Safe context

623K

Memory

6.2 GB / 24.0 GB

Memory breakdown

Weights2.4 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsgemma 3 4b it on NVIDIA L4 24GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 64.0 tok/s decode · 3.0s TTFT (warm) · 160 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	64.0 tok/s	1650 ms	623K
Coding	C	Runs well	64.0 tok/s	3025 ms	623K
Agentic Coding	C	Runs well	64.0 tok/s	4400 ms	623K
Reasoning	C	Runs well	64.0 tok/s	3575 ms	623K
RAG	C	Runs well	64.0 tok/s	5500 ms	623K

Inference speed

gemma 3 4b it inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for gemma 3 4b it at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~76 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	76.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	64.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	64.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	56.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	56.0	Fits
RTX 3060 12GB	12 GB	Q4_K_M	56.0	Fits
RTX 4060 8GB	8 GB	Q4_K_M	56.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	56.0	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	56.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	56.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	56.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	56.0	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	56.0	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	56.0	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	56.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	56.0	Fits

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How gemma 3 4b it (4B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.6 GB	Low	C44
Q3_K_S	3	2.0 GB	Low	C44
NVFP4	4	2.2 GB	Medium	C44
Q4_K_M	4	2.4 GB	Medium	C44
Q5_K_M	5	2.9 GB	High	C45
Q6_K	6	3.3 GB	High	C45
Q8_0	8	4.3 GB	Very High	C45
F16Best for your GPU	16	8.2 GB	Maximum	C48

Get started

Copy-paste commands to run gemma 3 4b it on your machine.

Run

lms load hf-lmstudio-community--gemma-3-4b-it-gguf && lms server start

Upgrade options

Hardware that runs gemma 3 4b it well

MacBook Pro M4 Max 36GBBudget pick

36 GB Unified (+12)410 GB/s (+110)

This setup is broadly balanced for this model.56 tok/s decode

~$2,499 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run gemma 3 4b it?

Yes, NVIDIA L4 24GB can run gemma 3 4b it with a C grade (Runs well). Expected decode speed: 64.0 tok/s.

How much VRAM does gemma 3 4b it need?

gemma 3 4b it (4B parameters) requires approximately 6.2 GB of memory with Q4_K_M quantization.

What is the best quantization for gemma 3 4b it?

The recommended quantization for gemma 3 4b it is Q4_K_M, which balances quality and memory efficiency.

What speed will gemma 3 4b it run at on NVIDIA L4 24GB?

On NVIDIA L4 24GB, gemma 3 4b it achieves approximately 64.0 tokens per second decode speed with a time-to-first-token of 3025ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run gemma 3 4b it for coding?

For coding workloads, gemma 3 4b it on NVIDIA L4 24GB receives a C grade with 64.0 tok/s and 623K context.

What context window can gemma 3 4b it use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, gemma 3 4b it can safely use up to 623K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA L4 24GB See all hardware for gemma 3 4b it

Embed this result▼

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<iframe src="https://willitrunai.com/embed/hf-lmstudio-community--gemma-3-4b-it-gguf-on-l4-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

64.0 tok/s

1650 ms

623K

Coding

Runs well

64.0 tok/s

3025 ms

623K

Agentic Coding

Runs well

64.0 tok/s

4400 ms

623K

Reasoning

Runs well

64.0 tok/s

3575 ms

623K

RAG

Runs well

64.0 tok/s

5500 ms

623K

gemma 3 4b it inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

76.0

Fits

RTX 4090 24GB

24 GB

Q4_K_M

64.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

64.0

Fits

RTX 3090 24GB

24 GB

Q4_K_M

56.0

Fits

RTX 4070 12GB

12 GB

Q4_K_M

56.0

Fits

RTX 3060 12GB

12 GB

Q4_K_M

56.0

Fits

RTX 4060 8GB

8 GB

Q4_K_M

56.0

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

56.0

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

56.0

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

56.0

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

56.0

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

56.0

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

56.0

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

56.0

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

56.0

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

56.0

Fits

Quant

Bits

VRAM

Quality

Fit

Q2_K

1.6 GB

Low

C44

Q3_K_S

2.0 GB

Low

C44

NVFP4

2.2 GB

Medium

C44

Q4_K_M

2.4 GB

Medium

C44

Q5_K_M

2.9 GB

High

C45

Q6_K

3.3 GB

High

C45

Q8_0

4.3 GB

Very High

C45

F16Best for your GPU

8.2 GB

Maximum

C48

Frequently asked questions

Can NVIDIA L4 24GB run gemma 3 4b it?

Yes, NVIDIA L4 24GB can run gemma 3 4b it with a C grade (Runs well). Expected decode speed: 64.0 tok/s.

How much VRAM does gemma 3 4b it need?

gemma 3 4b it (4B parameters) requires approximately 6.2 GB of memory with Q4_K_M quantization.

What is the best quantization for gemma 3 4b it?

The recommended quantization for gemma 3 4b it is Q4_K_M, which balances quality and memory efficiency.

What speed will gemma 3 4b it run at on NVIDIA L4 24GB?

On NVIDIA L4 24GB, gemma 3 4b it achieves approximately 64.0 tokens per second decode speed with a time-to-first-token of 3025ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run gemma 3 4b it for coding?

For coding workloads, gemma 3 4b it on NVIDIA L4 24GB receives a C grade with 64.0 tok/s and 623K context.

What context window can gemma 3 4b it use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, gemma 3 4b it can safely use up to 623K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.